Scope of Study

CHAPTER 2: LITERATURE REVIEW

3.1 Scope of Study

This study including the samples of 59 banks in 13 countries that exporting oil such as Algeria, Gabon, Qatar, Norway, Russia, Iraq, Saudi Arabia, UAE, Malaysia, Nigeria, Canada, Kuwait, and United States as cross-sectional data. Meanwhile, the time series data is within the period 2014 and 2018. This study has 3 explanatory variables which are debtor collection period, creditor payment period and liquidity buffer as well as an explained variable which is liquidity risk of banks.

Table 3.1. Data sources

Variables Proxy Unit Measurement Sources

Liquidity Risk Ratio of the liquid asset to total asset

Ratio Bloomberg

Debtors Collection Period

Ratio of the loan of the banks to net income

Years Bloomberg

Creditors

Payment Period

Ratio of total deposits to cost of Sales

Years Bloomberg

Liquidity Buffer

Ratio of liquid asset to total deposits and short-term funding

Ratio Bloomberg

Note. The 13 countries act as the sample is including Algeria, Gabon, Qatar, Norway, Russia, Iraq, Saudi Arabia, UAE, Malaysia, Nigeria, Canada, Kuwait, and United States.

Undergraduate Research Project Page 27 of 58 Faculty Business and Finance

3.2 Research Framework

Research framework adopted by Kimani, Nyangáu, Karungu and Kirui (2014).

The linear regression model is specified as below:

LR = f (DCP, CPP, LB)

LR = β₀α+β₁𝐷𝐶𝑃_𝑖𝑡+β₂𝐶𝑃𝑃_𝑖𝑡+β₃𝐿𝐵_𝑖𝑡+ε_𝑖𝑡

(Equation 1)

Where:

LR = Liquidity Risk DCP= Debtors Collection Period

CPP= Creditors Payment Period LB= Liquidity Buffer

β₀ = Intercept constant

β₁,β₂& β₃= Partial regression coefficients of the scope of the regression line of the independent variables 1 to 3. They show the correspondence between the independent and dependent variables

𝜀 = error term

The PVAR model will be used to include the data. It has included 295 of observations and 5 years of data. Generalized Method of Moments (GMM) can be used in our model.

Undergraduate Research Project Page 28 of 58 Faculty Business and Finance (

𝑦_𝑖𝑡 𝐷𝐶𝑃_𝑖𝑡 𝐶𝑃𝑃_𝑖𝑡 𝐿𝐵_𝑖𝑡

)=(

𝐷𝐶𝑃_𝑖𝑡 𝐶𝑃𝑃_𝑖𝑡 𝐿𝐵_𝑖𝑡 𝑦_𝑖𝑡 𝐶𝑃𝑃_𝑖𝑡 𝐿𝐵_𝑖𝑡 𝐷𝐶𝑃_𝑖𝑡 𝑦_𝑖𝑡 𝐿𝐵_𝑖𝑡 𝐷𝐶𝑃_𝑖𝑡 𝐶𝑃𝑃_𝑖𝑡 𝑦_𝑖𝑡

) ( 𝛽₁₁ 𝛽₁₂ 𝛽₁₃

)+….+

(

𝐷𝐶𝑃_{𝑖𝑡−𝑛} 𝐶𝑃𝑃_{𝑖𝑡−𝑛} 𝐿𝐵_{𝑖𝑡−𝑛} 𝑦_{𝑖𝑡−𝑛} 𝐶𝑃𝑃_{𝑖𝑡−𝑛} 𝐿𝐵_{𝑖𝑡−𝑛} 𝐷𝐶𝑃_{𝑖𝑡−𝑛} 𝑦_{𝑖𝑡−𝑛} 𝐿𝐵_{𝑖𝑡−𝑛} 𝐷𝐶𝑃_{𝑖𝑡−𝑛} 𝐶𝑃𝑃_{𝑖𝑡−𝑛} 𝑦_{𝑖𝑡−𝑛}

) ( 𝛽₁₄ 𝛽₁₅ 𝛽₁₆

)+(

𝛼_𝑖𝑡 𝛼_𝑖𝑡 𝛼_𝑖𝑡 𝛼_𝑖𝑡

)+(

𝜀_1𝑖𝑡 𝜀_2𝑖𝑡 𝜀_3𝑖𝑡 𝜀_4𝑖𝑡

)

(Equation 2) α and β= unknown coefficient

In the case to solve the issue of endogeneity issue in PVAR, GMM model can use the equation shown as above. Since it is difficult to interpret the coefficient of the PVAR, thus the variance decomposition and impulse-response functions will be created in order to determine the response and the effect of the variables.

Furthermore, the individual effects can be removed by Helmert Transformation or called as Forward Orthogonal Deviation (FOD).

Helmert Transformation:

𝐿𝑅_𝑖𝑡^∗ = 𝛼₁𝐿𝑅_𝑖𝑡−1^∗ + 𝛼₂𝐷𝐶𝑃_𝑖𝑡−1^∗ + 𝛼₃𝐶𝑃𝑃_𝑖𝑡−1^∗ + 𝛼₄𝐿𝐵_𝑖𝑡−1^∗ 𝑚_𝑖𝑡^∗=(mit - 𝑚̅̅̅̅̅ )√𝑇_{𝑖𝑡 𝑖𝑡}/(𝑇_𝑖𝑡+ 1)

(Equation 3)

𝑚̅= average of all variable observation, 𝑇_𝑖𝑡= total of number variable observations

Table 3.2. Expected relationship between variables

Explanatory variables Relationship with Liquidity Risk Debtors Collection Period Positive

Undergraduate Research Project Page 29 of 58 Faculty Business and Finance Creditors Payment Period Negative

Liquidity Buffer Negative

The dependent variables of liquidity risk can use the liquidity ratio as the measurement. Liquid Asset to Total Asset Ratio is the effective and quickly way to know the liquidity status of the bank. This ratio also provides the measurement regarding on their ability in liquidity status when demand for cash (Sathyamoorthi1, Mapharing & Dzimiri,2020). The liquidity higher means that the liquidity risk will lower, since the bank having enough liquidity asset and ability when demand for cash, hence lower their liquidity risk.

3.2.1 Unit Root Test

The unit root test will apply Fisher Augmented Dickey Fuller Test (Fisher ADF), Fisher Phillips, Perron (Fisher PP) Test and Im, Pesaran, Shin (IPS) Test. The reason for using Fisher ADF test as it will involve those delayed values of dependent variables, as well as considering the autocorrelation issues. Fisher PP test can be called a revised version of the ADF test since it takes more consideration.

Fisher ADF and Fisher PP is considered as a singular unit root test since they calculated separately for each country meanwhile IPS test is suitable for panel unit root test. Different with ADF, IPS consider all panel unit root tests and the average score of panel members (Firat,2016).

3.2.2 Lag Order Selection

Undergraduate Research Project Page 30 of 58 Faculty Business and Finance By reviewing the past studies of panel data models, the panel data can be estimated by using the Generalized Method of Moments (GMM). GMM can help to choose the correct model and specification, as well as maintain the consistency. According to Andrew and Lu (1999), they suggested Model and Moment Selection Criteria (MMSC) to complete the GMM estimation. Bayesian Information Criteria (BIC), Akaike Information Criteria (AIC), and Hannan Quinn Information Criteria (HQIC) is the preferable selection method in MMSC. MMSC is based on J test statistics and can help to reduce the over-identifying problem. For the lag order selection, the minimum of MAIC, MBIC and MHQIC will be chosen as the optimal lag order selection.

3.2.3 PVAR Estimation

First and foremost, identify the lag order of endogenous variables and lag value of endogenous variables as variables to estimate to construct the PVAR model. The optimal lag order of PVAR model is determined. The PVAR lag order minimizes the above c is recognized as the optimal lag order. The results show that first-order values are the smallest among all the statistical values of the criteria. Therefore, the first-order value is chosen as the suitable lag order to construct the PVAR model.

After that, to estimate this, the over-identifying restriction test was run to ensure that the 𝐻₀ is not rejected in order to prove that the variables in the PVAR model is not overidentify when using Hansen’s J 𝑐ℎ𝑖² to test it. Thus, the estimation is valid in the model if it is overidentified.

3.2.4 Granger Causality

Undergraduate Research Project Page 31 of 58 Faculty Business and Finance The function of causality test is to analyze the causal relationship between the variables, also to check that if one time series variables is meaningful to predict the variables. (Wang, 2016) Granger causality is a recommended method in the research of econometric models. The characteristics of this test can take consideration of all interaction between variables during the period unit observation, and it also can show clear view regarding the causal chain of the variables (Oluwapelumi & Olaride, 2017). Granger Causality Wald test can help to find the causality between the variables. For the hypothesis test, when probability value is lower than any α level, thus the null hypothesis will be rejected (Wang, 2016).

The hypothesis shown as below:

H0: Excluded variable does not granger-cause equation variable H1: Excluded variable granger-causes equation variable

3.2.5 PVAR Impulse Response-Functions

In the research study, it will concentrate on impulse response-functions for estimated panel VAR model, which indicates the effect of one variable in the system to the innovations in another variable in the system, holding there is no shock. It is constructed from the estimated VAR coefficients and the standard errors.

Additionally, the impulse variables must be listed and specified to introduce and evaluate the response for all exogenous variables. In the equation (4), the exogenous variables are in the autoregressive structure of the panel VAR to preserve generality.

Based on Hamilton (1994), he stated that if every moduli of their companion matrices 𝐴̅ present value less than one, it can produce the stable VAR models. Each companion matrix is computed by:

Undergraduate Research Project Page 32 of 58 Faculty Business and Finance 𝐴̅ =

[

𝐴₁ 𝐴₂ ⋯ 𝐴_𝑝 𝐴_𝑝−1 𝐼_𝑘 0_𝑘 ⋯ 0_𝑘 0_𝑘 0_𝑘 0_𝑘 ⋯ 0_𝑘 0_𝑘

⋮ ⋮ ⋱ ⋮ ⋮

0_𝑘 0_𝑘 ⋯ 𝐼_𝑘 0_𝑘 ]

(Equation 4)

The panel VAR able to have vector moving-average (VMA) representation and reversible if the VAR model is stable. Besides, Panel VAR can present the estimated impulse-response functions (IRF) and the forecast-error variance decompositions.

The model could be revised as an infinite vector moving-average (VMA), to evaluate a simple impulse-response function ɸ _𝑖 , ɸ _𝑖 refer to parameters of VMA.

ɸ_𝑖= {

𝐼_𝑘, 𝑖 = 0

∑ ɸ_𝑡−𝑗𝐴_𝑗,

𝑖

𝑗=1

𝑖 = 1,2, …

(Equation 5)

Simple IRFs will not come out with the causal interpretation. Another shock on variable will be induced by a variable shock because of the correlation of 𝑒_𝑖𝑡 that happen in identical moment. By giving that matrix P, the orthogonalized impulse responses 𝑃ɸ_𝑖 will be transformed from P’P=Σ, VMA parameters by inducing P to orthogonalize the innovations as 𝑒_𝑖𝑡𝑃 − 1. The system of dynamic equations can apply the identification of restrictions effectively by using matrix P (Abrigo & Love, 2015).

The Cholesky decomposition of Σ is adhere to the variables’ order in Σ is not particular, but the function can be employed in applying about repetitive structure on a VAR (Sims, 1980).

Undergraduate Research Project Page 33 of 58 Faculty Business and Finance

3.2.6 Panel Variance Decomposition

In this research study, the variance decomposition is presented to explain the degree of the overall effect of a shock, providing the movement proportion of one variable can be explained the shock to another variable over time. Additionally, the stimulation of the standard deviation and confidence interval and the variance decomposition of the prediction error of the impulse response equation is to generate the variance analysis result and the impulse response function diagram.

Here shows the equation:

𝑌_𝑖𝑡+ℎ− 𝐸[𝑌_𝑖𝑡+ℎ] = ∑ 𝑒_{𝑖(𝑡+ℎ−𝑖)}ɸ_𝑖

ℎ−1

𝑖=0

(Equation 6)

Where, 𝑌_𝑖𝑡+ℎ is the observed vector at time t+ℎ while 𝐸[𝑌_𝑖𝑡+ℎ] indicates the ℎ-step before estimated vector constructed during time t. The matrix P can be used to orthogonalize the shocks, thus 𝐼_𝑘 can be covariance matrix of the orthogonalized shocks 𝑒_𝑖𝑡𝑃⁻¹. The estimated-error variance will have the disintegration in the direct way of the because of the process of orthogonalization. At the same time, matrix P enables the contribution separation for every factor to the variance of estimated-error. The impact of a certain factor on the estimated-error variance of factor contribution can be gained through the formula below:

∑ 𝜃²𝑚𝑛

ℎ−1

𝑖=0

= ∑(𝑖^′_𝑛𝑃ɸ_𝑖𝑖_𝑚)²

ℎ−1

𝑖=1

(Equation 7)

Undergraduate Research Project Page 34 of 58 Faculty Business and Finance Where, 𝐼_𝑠is s-th column of 𝐼_𝑘. However, the impacts are generally normalized depends on the estimated-error variance in the reality (Abrigo & Love, 2015).

∑ 𝜃_.𝑛² = ∑ 𝑖′_𝑛ɸ_𝑖′Σɸ_𝑖𝑖_𝑛

ℎ−1

𝑖=1 ℎ−1

𝑖=0

(Equation 8)